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Dimensionality reduction for high dimensional data
Proceedings of the 2014 International Conference on Information and Communication Technology for Competitive Strategies, 2014Information Technology has produced huge amounts of data and these data need to be processed to extract information hidden in it. Feature selection techniques often come handy to process these data efficiently. In this paper, a novel approach for feature selection GA-CFS is proposed.
Aditya Kumar, Smita Roy, Prabhat Ranjan
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Dynamics of ICA for High- Dimensional Data
2002The learning dynamics close to the initial conditions of an on-line Hebbian ICA algorithm has been studied. For large input dimension the dynamics can be described by a diffusion equation.A surprisingly large number of examples and unusually low initial learning rate are required to avoid a stochastic trapping state near the initial conditions.
Basalyga, Gleb, Rattray, Magnus
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American Journal of Orthodontics and Dentofacial Orthopedics, 2023
Melvin Geubbelmans +3 more
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Melvin Geubbelmans +3 more
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A partitioning method for high dimensional data
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication, 2010Nearest neighbor search in high-dimensional space is an important operation in many applications, such as data mining and multimedia database. Evaluating similarities of a point to all other points in high-dimensional space need the high computational cost. For reducing the computational cost, index-structures are frequently used.
Seunghoon Lee +4 more
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High-dimensional Data Visualization
2007One of the biggest challenges in data visualization is to find general representations of data that can display the multivariate structure of more than two variables. Several graphic types like mosaicplots, parallel coordinate plots, trellis displays, and the grand tour have been developed over the course of the last three decades.
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Learning high-dimensional multimedia data
Multimedia Systems, 2016The internet revolution has made information acquisition easy and cheap and has been producing massive high-dimensional multimedia data, including text, audio, images, animation, video, etc. High-dimensional multimedia data bring new opportunities to modern society and challenges to researchers of the multimedia domain as well. The goal of this special
Xiaofeng Zhu 0001 +2 more
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2019
We have a dataset that is a collection of d-dimensional vectors. This chapter introduces the nasty tricks that such data can play. A dataset like this is hard to plot, though Sect. 4.1 suggests some tricks that are helpful. Most readers will already know the mean as a summary (it’s an easy generalization of the 1D mean).
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We have a dataset that is a collection of d-dimensional vectors. This chapter introduces the nasty tricks that such data can play. A dataset like this is hard to plot, though Sect. 4.1 suggests some tricks that are helpful. Most readers will already know the mean as a summary (it’s an easy generalization of the 1D mean).
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Querying high-dimensional data in single-dimensional space
The VLDB Journal The International Journal on Very Large Data Bases, 2004In this paper, we propose a new tunable index scheme, called iMinMax($\theta$), that maps points in high-dimensional spaces to single-dimensional values determined by their maximum or minimum values among all dimensions. By varying the tuning “knob”, $\theta$, we can obtain different families of iMinMax structures that are optimized for different ...
Cui Yu +3 more
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On High Dimensional Indexing of Uncertain Data
2008 IEEE 24th International Conference on Data Engineering, 2008In this paper, we will examine the problem of distance function computation and indexing uncertain data in high dimensionality for nearest neighbor and range queries. Because of the inherent noise in uncertain data, traditional distance function measures such as the Lq-metric and their probabilistic variants are not qualitatively effective.
Charu C. Aggarwal, Philip S. Yu
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Clustering of High Dimensional Data Streams
2012Clustering of data streams has become a task of great interest in the recent years as such data formats is are becoming increasingly ambiguous. In many cases, these data are also high dimensional and in result more complex for clustering. As such there is a growing need for algorithms that can be applied on streaming data and the at same time can cope ...
Sotiris K. Tasoulis +2 more
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